Audio annotation · Accent Adaptation
Audio Annotation for Accent Adaptation
Labelling of existing audio: speaker diarisation, emotion, intent, events, language identification and segment-level quality tagging, against your label schema. Applied to accent adaptation, the specification is driven by one thing: per-accent wer spread.
- Service
- Audio annotation
- Use case
- Accent Adaptation
- Primary metric
- Per-accent WER spread
Required data profile
- Accent-band balanced speech with substrate-language tags
- Matched content across bands for controlled comparison
Technical specification
| Parameter | Standard |
|---|---|
| Label types | Diarisation, emotion, intent, events, language ID, quality |
| Granularity | Segment, utterance, or frame-level boundaries |
| Schema | Yours, or authored with you before work starts |
| Agreement | Multi-annotator overlap on a defined percentage |
| Tooling | Client tooling supported; otherwise our annotation workflow |
Process
- Schema definition and edge-case documentation
- Annotator training and gold-set calibration
- Production annotation with gold items seeded in
- Adjudication of disagreements by a senior reviewer
- Delivery with per-label agreement statistics
Metrics this feeds
- Per-accent WER spread
- Regression on the original accent set
Failure modes to design out
- Treating Indian English as one accent
- No substrate tagging, so the model cannot be evaluated per band
Gold items are seeded throughout production so drift is caught during the run, not at delivery.
Deliverables
- Labelled data in your schema
- Gold set and calibration results
- Per-label agreement statistics
- Edge-case log
Frequently asked
Is audio annotation the right service for accent adaptation?
It covers accent-band balanced speech with substrate-language tags. Most accent adaptation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.
What languages are available?
All 14 languages in the network plus Indian English accent bands.
How is the evaluation set handled?
Collected first, from speakers disjoint from the training cohort, so improvement is measurable.
Scope audio annotation for accent adaptation
Send the metric you need to move.